ArticleQuantitative imaging in medicine and surgery2019
Quantitative features can predict further growth of persistent pure ground-glass nodule.
Article in Quantitative imaging in medicine and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers, 3 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Risk factors for the growth of ground-glass nodules in the lungs: A systematic review and meta-analysis.Clinics (Sao Paulo, Brazil) · 2025Pooled it
- The long-term course of subsolid nodules and predictors of interval growth on chest CT: a systematic review and meta-analysis.European radiology · 2023Pooled it
- Clinical and CT Features of Subsolid Pulmonary Nodules With Interval Growth: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- A narrative review on CT-based evaluation and prediction of lung nodule growth: current status and future directions.Translational lung cancer research · 2026Review
- East-West Disparities in Lung Cancer Screening: Subsolid Nodule Prevalence, Interval Growth, and Decision-Making Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Performance of multi-planar volume rendering versus maximum intensity projection and minimum intensity projection on pulmonary nodule detection.Quantitative imaging in medicine and surgery · 2026Article
- Preoperative CT-based radiomics to predict the 5-year growth of residual nodules after resection of dominant lung tumors in patients with multiple lung subsolid nodules.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Predicting Invasiveness of Lung Adenocarcinoma from Chest CT with Few-shot Vision-Language Ternary Classification Model.NPJ digital medicine · 2025Article
- Article
- Tumor blood vessel in 3D reconstruction CT imaging as an risk indicator for growth of pulmonary nodule with ground-glass opacity.Journal of cardiothoracic surgery · 2023Article
- A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence.Journal of cancer research and clinical oncology · 2023Article
- Natural progression of persistent pure ground-glass nodules 10 mm or smaller: long-term observation and risk factor assessment.Japanese journal of radiology · 2023Article
- Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study.La Radiologia medica · 2023Article
- [Growth Regularity of Pulmonary Ground Glass Nodules Based on 3D Reconstruction Technology].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2023Article
- Article
- Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study.Translational lung cancer research · 2022Article
- The natural growth history of persistent pulmonary subsolid nodules: Radiology, genetics, and clinical management.Frontiers in oncology · 2022Review
- Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules.Computational and mathematical methods in medicine · 2022Article
- Natural history of pathologically confirmed pulmonary subsolid nodules with deep learning-assisted nodule segmentation.European radiology · 2021Article
- Combination of generative adversarial network and convolutional neural network for automatic subcentimeter pulmonary adenocarcinoma classification.Quantitative imaging in medicine and surgery · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo evaluate whether quantitative features of persistent pure ground-glass nodules (PGGN) on the initial computed tomography (CT) scans can predict further nodule growth.
methodsThis retrospective study included 59 patients with 101 PGGNs from 2011 to 2012, who received regular CT follow-up for lung nodule surveillance. Nineteen quantitative image features consisting of 8 volumetric and 11 histogram parameters were calculated to detect lung nodule growth. For the extraction of the quantitative features, semi-automatic GrowCut segmentation was implemented on chest CT images in 3D slicer platform. Univariate and multivariate analyses were performed to identify risk factors for nodule growth.
resultsWith a median follow-up of 52 months, nodule growth was detected in 10 nodules by radiological assessment and in 16 nodules by quantitative features. In univariate analysis, 3D maximum diameter (MD), volume, mass, surface area, 90% percentile, and standard deviation value (SD) of PGGN on the initial CT scan were significantly different between stable nodules and nodules with further growth. In multivariate analysis, MD [hazard ratio (HR), 3.75; 95% confidence interval (CI), 2.14-6.55] and SD (HR, 2.06; 95% CI, 1.35-3.14) were independent predictors of further nodule growth. Also, the area under the curve was 0.896 (95% CI: 0.820-0.948) and 0.813 (95% CI: 0.723-0.883) for MD with a cut-off value of 10.2mm and SD of 50.0 Hounsfield Unit (HU). Besides, the growth rate was 55.6% (n=15) of PGGNs with MD >10.2 mm and SD >50.0 HU.
conclusionsBased on the initial CT scan, the quantitative features can predict PGGN growth more precisely. PGGN with MD >10.2 mm and SD >50.0 HU may require close follow-up or surgical intervention for the high incidence of growth.
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